This article presents two simple double robust estimators of a mean ("marginal") model when subjects are longitudinally censored. In our example, we were interested in modeling the probability of negative health outcomes in subjects with HIV under the scenario where all subjects remained on treatment that successfully controlled viral load. Subjects were artificially censored when they did not continue on treatment, which therefore required modeling adjustment.

A new article entitled "The causal inference paradigm for network meta-analysis with implications for feasibility and practice" is currently available (pre-peer review) on arXiv stat: http://arxiv.org/abs/1506.01583Network meta-analysis involves the aggregation of study results in order to contrast the effects of different treatments for a common condition. While standard meta-analysis only summarizes over one pair of treatment contrasts, network meta-analysis can combine a multitude of different treatments.

In this article, we define a nonparametric "effect of interest" in a network meta-analysis over heterogeneous underlying populations. We then develop a set of restrictions that allow for estimation of this effect. We propose several estimators and compare their realistic small-sample performance in a simulation study. Finally, we demonstrate this approach in a real data example to evaluate the relative effectiveness of antibiotics on methicillin-resistant Staphylococcus aureus (MRSA) infection.

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